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Long-Term Memory and Applying the Multi-Factor ARFIMA Models in Financial Markets

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Abstract

Exploiting the classical R/S and modified R/S analysis, we first reveal theevidence of long-term memory in liquidity, volume, and volatility. Thereafter,we estimate the fractionally integrated autoregressive movingaverage ARFIMA models by both the exact-maximum likelihood (EML) and themodified-profile likelihood (MPL) methods. Furthermore, based on the theoryof financial economics, we extend the simple ARFIMA models to the Multi-FactorARFIMA models by incorporating the mutual relationships among financial marketvariables and present the effectiveness of the Multi-Factor ARFIMA models infinancial markets.

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Authors and Affiliations

  1. Faculty of Business Administration, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Siga, 525-8577, Japan

    Chikashi Tsuji

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  1. Chikashi Tsuji
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Tsuji, C. Long-Term Memory and Applying the Multi-Factor ARFIMA Models in Financial Markets. Asia-Pacific Financial Markets 9, 283–304 (2002). https://doi.org/10.1023/A:1024105822304

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